Training AI in healthcare and finance often hits a wall: you need more data than you can ethically or legally collect. Real medical records or financial transactions are subject to strict regulations like HIPAA and GDPR. Pulling them into your model pipeline is expensive, technically complex, and risky. Synthetic data offers a way to scale your AI without exposing a single real patient or transaction.
Synthetic data is not just random numbers
A common misconception is that synthetic data means generating random values and hoping for the best. The most effective approaches are grounded in real statistical distributions. For example, a synthetic EHR dataset might preserve the exact prevalence of hypertension, diabetes, and age ranges found in a real hospital system. It should also reflect realistic correlations: patients with hypertension are more likely to have diabetes, not less. This means synthetic datasets can be statistically indistinguishable from real data while containing no actual patient information.
Regulatory compliance through design
Synthetic data can be designed to be inherently compliant. Because no personally identifiable information is present, the need for data access agreements and de-identification processes is often dramatically reduced. In practice, many companies build synthetic datasets directly into their governance framework. They define the schema, allowable ranges, and relationships at the outset, ensuring that every record produced matches regulatory requirements before it is ever used to train a model.
Real performance gains from synthetic training data
Synthetic data can help you reach production-ready models faster. A large language model for clinical notes might achieve only 78% accuracy on a small real dataset. When trained on a synthetic dataset that mimics the distribution of that notes corpus, accuracy can jump to 85% in a single iteration. Similarly, synthetic financial transaction data can be used to train anomaly detection models quickly. Teams often report that synthetic data reduces the time between data collection and model validation by 40% or more.
Evaluating AI with realistic synthetic trajectories
Beyond tabular data, synthetic trajectories are powerful for evaluating computer-use agents. In finance, an agent that processes loan applications or manages portfolios benefits from synthetic interaction histories that mirror real workflows. In healthcare, agents that assist with documentation or patient triage can be evaluated on synthetic logs that show realistic decisions, errors, and retry patterns. These synthetic logs are often indistinguishable from production data, but they are completely safe to use for stress testing and benchmarking.
Key tradeoffs to watch
Coverage and diversity: synthetic data must cover the edge cases your real data misses. If your real data lacks rare diseases or complex financial products, your synthetic data must be designed to fill those gaps.Correlation accuracy: the model will learn from whatever relationships you encode. Poorly modeled correlations can lead to biased outputs.Domain expertise: synthetic data generation benefits from deep domain knowledge. Subject matter experts should help define the schema, ranges, and realistic workflows.Evaluation rigor: just because data is synthetic does not mean it is automatically safe. You still need to validate that synthetic data preserves the statistical properties that matter for your use case.
The takeaway: synthetic data can dramatically expand the data you have for healthcare and finance AI, reduce compliance risk, and accelerate model development, all while keeping sensitive information completely private.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This allows the team to understand complex workflows across healthcare and finance systems. Using those insights, Coasty can produce custom synthetic datasets and trajectories tailored to your specific needs. This is a custom, contact-led service: you work directly with the Coasty data team to design the right synthetic data pipeline for your project.
If you are ready to leverage privacy safe synthetic data for healthcare and finance AI, talk to the Coasty data team to explore how custom synthetic datasets can support your use case. Book a data call at https://cal.com/coasty/coasty-data-call.
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